I’ve been experimenting with a dynamic hyperparameter optimization method that uses real-time simulation feedback to adjust training parameters (learning rate, non-local interaction strength, etc.). Early results show promising improvements:
*15% faster convergence on Wikitext and OpenWebText benchmarks
*20% reduction in training loss variance
*10–15% compute savings via targeted adjustments
The system identifies critical thresholds in network behavior (e.g., cohesion metrics) to trigger updates, avoiding manual tuning. Interestingly, models exhibit more stable, “human-like” learning trajectories—less catastrophic forgetting, better open-ended task performance.
Open questions for the community:
. *How would you measure “human-like” learning in LLMs?
. *Has anyone seen similar gains with non-static hyperparameter schedules?
. *Are there benchmarks for creativity/adaptability in text generation?
I’m open to collaboration/feedback—DM if you’d like to discuss!